{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入所需的python库 \n",
    "import sys\n",
    "import warnings\n",
    "if not sys.warnoptions:\n",
    " warnings.simplefilter(\"ignore\")\n",
    "import datetime\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, LSTM\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Stock symbol</th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Last</th>\n",
       "      <th>Rise and fall</th>\n",
       "      <th>Price%</th>\n",
       "      <th>Volume (lots)</th>\n",
       "      <th>Turnover (1,000 yuan)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>20200724</td>\n",
       "      <td>27.35</td>\n",
       "      <td>27.88</td>\n",
       "      <td>26.85</td>\n",
       "      <td>27.03</td>\n",
       "      <td>27.52</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-1.7805</td>\n",
       "      <td>1160239.76</td>\n",
       "      <td>3174571.069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>20200723</td>\n",
       "      <td>27.64</td>\n",
       "      <td>27.98</td>\n",
       "      <td>27.11</td>\n",
       "      <td>27.52</td>\n",
       "      <td>27.88</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-1.2912</td>\n",
       "      <td>991225.69</td>\n",
       "      <td>2723562.572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>20200722</td>\n",
       "      <td>27.89</td>\n",
       "      <td>28.30</td>\n",
       "      <td>27.65</td>\n",
       "      <td>27.88</td>\n",
       "      <td>28.01</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.4641</td>\n",
       "      <td>882146.50</td>\n",
       "      <td>2470127.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>20200721</td>\n",
       "      <td>28.45</td>\n",
       "      <td>28.53</td>\n",
       "      <td>27.84</td>\n",
       "      <td>28.01</td>\n",
       "      <td>28.30</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-1.0247</td>\n",
       "      <td>776308.44</td>\n",
       "      <td>2177172.136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>20200720</td>\n",
       "      <td>27.39</td>\n",
       "      <td>28.50</td>\n",
       "      <td>27.29</td>\n",
       "      <td>28.30</td>\n",
       "      <td>27.23</td>\n",
       "      <td>1.07</td>\n",
       "      <td>3.9295</td>\n",
       "      <td>1132749.83</td>\n",
       "      <td>3171353.300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>20200717</td>\n",
       "      <td>27.26</td>\n",
       "      <td>27.64</td>\n",
       "      <td>26.91</td>\n",
       "      <td>27.23</td>\n",
       "      <td>27.54</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-1.1256</td>\n",
       "      <td>1166350.74</td>\n",
       "      <td>3177245.453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>20200716</td>\n",
       "      <td>28.25</td>\n",
       "      <td>28.72</td>\n",
       "      <td>27.52</td>\n",
       "      <td>27.54</td>\n",
       "      <td>28.28</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>-2.6167</td>\n",
       "      <td>1438432.10</td>\n",
       "      <td>4055153.708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>20200715</td>\n",
       "      <td>29.00</td>\n",
       "      <td>29.28</td>\n",
       "      <td>28.28</td>\n",
       "      <td>28.28</td>\n",
       "      <td>28.94</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-2.2806</td>\n",
       "      <td>1154989.79</td>\n",
       "      <td>3306443.715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>20200714</td>\n",
       "      <td>29.14</td>\n",
       "      <td>29.50</td>\n",
       "      <td>28.45</td>\n",
       "      <td>28.94</td>\n",
       "      <td>29.20</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.8904</td>\n",
       "      <td>1343018.28</td>\n",
       "      <td>3889222.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>20200713</td>\n",
       "      <td>28.92</td>\n",
       "      <td>29.39</td>\n",
       "      <td>28.48</td>\n",
       "      <td>29.20</td>\n",
       "      <td>29.17</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.1028</td>\n",
       "      <td>1804033.06</td>\n",
       "      <td>5218016.833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Stock symbol      Date   Open   High    Low  Close   Last  Rise and fall  \\\n",
       "0    000002.SZ  20200724  27.35  27.88  26.85  27.03  27.52          -0.49   \n",
       "1    000002.SZ  20200723  27.64  27.98  27.11  27.52  27.88          -0.36   \n",
       "2    000002.SZ  20200722  27.89  28.30  27.65  27.88  28.01          -0.13   \n",
       "3    000002.SZ  20200721  28.45  28.53  27.84  28.01  28.30          -0.29   \n",
       "4    000002.SZ  20200720  27.39  28.50  27.29  28.30  27.23           1.07   \n",
       "5    000002.SZ  20200717  27.26  27.64  26.91  27.23  27.54          -0.31   \n",
       "6    000002.SZ  20200716  28.25  28.72  27.52  27.54  28.28          -0.74   \n",
       "7    000002.SZ  20200715  29.00  29.28  28.28  28.28  28.94          -0.66   \n",
       "8    000002.SZ  20200714  29.14  29.50  28.45  28.94  29.20          -0.26   \n",
       "9    000002.SZ  20200713  28.92  29.39  28.48  29.20  29.17           0.03   \n",
       "\n",
       "   Price%  Volume (lots)  Turnover (1,000 yuan)  \n",
       "0 -1.7805     1160239.76            3174571.069  \n",
       "1 -1.2912      991225.69            2723562.572  \n",
       "2 -0.4641      882146.50            2470127.024  \n",
       "3 -1.0247      776308.44            2177172.136  \n",
       "4  3.9295     1132749.83            3171353.300  \n",
       "5 -1.1256     1166350.74            3177245.453  \n",
       "6 -2.6167     1438432.10            4055153.708  \n",
       "7 -2.2806     1154989.79            3306443.715  \n",
       "8 -0.8904     1343018.28            3889222.008  \n",
       "9  0.1028     1804033.06            5218016.833  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(r'C:\\Users\\86166\\Desktop\\000002.SZ.xls')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    20200724\n",
       "1    20200723\n",
       "2    20200722\n",
       "3    20200721\n",
       "4    20200720\n",
       "Name: Date, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将索引设置为时间格式方便后续分析\n",
    "#转换格式\n",
    "df.Date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    20200724\n",
       "1    20200723\n",
       "2    20200722\n",
       "3    20200721\n",
       "4    20200720\n",
       "Name: Date, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.astype({'Date':'str'})\n",
    "df.Date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Date\"] = pd.to_datetime(df.Date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2020-07-24\n",
       "1   2020-07-23\n",
       "2   2020-07-22\n",
       "3   2020-07-21\n",
       "4   2020-07-20\n",
       "Name: Date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = df['Date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看一下近五年的收盘价变动\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.grid()\n",
    "dstart = datetime.datetime(2015,1,1)\n",
    "dstop = datetime.datetime(2021,1,1)\n",
    "plt.ylim(10,50)\n",
    "plt.xlim(dstart,dstop)\n",
    "plt.plot(df['Close'], label='Close Price history')\n",
    "plt.savefig(r'C:\\Users\\86166\\Desktop\\TaTa股价变动.png',\n",
    " dpi=300, bbox_inches='tight')\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df.sort_index(ascending=True, axis=0)\n",
    "new_data = pd.DataFrame(index=range(0,len(df)),\n",
    " columns=['Date', 'Close'])\n",
    "for i in range(0,len(data)):\n",
    " new_data['Date'][i] = data['Date'][i]\n",
    " new_data['Close'][i] = data['Close'][i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置dataframe索引\n",
    "new_data.index = new_data.Date\n",
    "new_data.drop('Date', axis=1, inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据分为训练集和测试集 \n",
    "dataset = new_data.values\n",
    "train = dataset[0:700,:]\n",
    "valid = dataset[700:,:]\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled_data = scaler.fit_transform(dataset)\n",
    "x_train, y_train = [], []\n",
    "for i in range(60,len(train)):\n",
    " x_train.append(scaled_data[i-60:i,0])\n",
    " y_train.append(scaled_data[i,0])\n",
    "x_train, y_train = np.array(x_train), np.array(y_train)\n",
    "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#创建长短期记忆神经网络\n",
    "model = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "640/640 - 9s - loss: 0.0057 - 9s/epoch - 14ms/step\n",
      "9/9 [==============================] - 1s 8ms/step\n"
     ]
    }
   ],
   "source": [
    "#模型为3层，前两层五十个神经元，最后一层为输出层，仅一个神经元\n",
    "\n",
    "model.add(LSTM(units=50, return_sequences=True, \n",
    " input_shape=(x_train.shape[1],1)))\n",
    "model.add(LSTM(units=50))\n",
    "model.add(Dense(1))\n",
    "#编译模型，以MSE为损失函数，adam优化算法 \n",
    "\n",
    "# 大多数情况下adam优化速度较快，结果也很好\n",
    "\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "# 将训练集带入模型，开始训练 \n",
    "\n",
    "model.fit(x_train, y_train, epochs=1, batch_size=1, verbose=2)\n",
    "#该模型训练时间约50秒\n",
    "\n",
    "# 应用训练好的模型预测股票信息\n",
    "\n",
    "inputs = new_data[len(new_data) - len(valid) - 60:].values\n",
    "inputs = inputs.reshape(-1,1)\n",
    "inputs = scaler.transform(inputs)\n",
    "X_test = []\n",
    "for i in range(60,inputs.shape[0]):\n",
    " X_test.append(inputs[i-60:i,0])\n",
    "X_test = np.array(X_test)\n",
    "X_test = np.reshape(X_test, (X_test.shape[0],X_test.shape[1],1))\n",
    "closing_price = model.predict(X_test)\n",
    "closing_price = scaler.inverse_transform(closing_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#模型预测\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rc('font', size=12)\n",
    "train = new_data[:700]\n",
    "valid = new_data[700:]\n",
    "valid['Predictions'] = closing_price\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.title(\"股票收盘价预测\")\n",
    "plt.grid()\n",
    "plt.plot(train['Close'],color=\"blue\",label=\"训练集\")\n",
    "plt.plot(valid['Close'],color=\"red\",label=\"测试集\")\n",
    "plt.plot(valid['Predictions'],color=\"orange\",label=\"预测结果\")\n",
    "plt.legend()\n",
    "plt.ylim(10,50)\n",
    "plt.xlim(dstart,dstop)\n",
    "plt.savefig(r'C:\\Users\\86166\\Desktop\\TaTa股价预测.png',\n",
    " dpi=300, bbox_inches='tight')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
